learning representations for counterfactual inference github

1 Paper Bayesian nonparametric modeling for causal inference. Here, we present Perfect Match (PM), a method for training neural networks for counterfactual inference that is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. zz !~A|66}$EPp("i n $* 167302 within the National Research Program (NRP) 75 "Big Data". individual treatment effects. BayesTree: Bayesian additive regression trees. Counterfactual inference enables one to answer "What if?" Cortes, Corinna and Mohri, Mehryar. Our deep learning algorithm significantly outperforms the previous state-of-the-art. (2011), is that it reduces the variance during training which in turn leads to better expected performance for counterfactual inference (Appendix E). How do the learning dynamics of minibatch matching compare to dataset-level matching? Swaminathan, Adith and Joachims, Thorsten. We selected the best model across the runs based on validation set ^NN-PEHE or ^NN-mPEHE. Most of the previous methods In these situations, methods for estimating causal effects from observational data are of paramount importance. The shared layers are trained on all samples. Quick introduction to CounterFactual Regression (CFR) Article . Approximate nearest neighbors: towards removing the curse of This work contains the following contributions: We introduce Perfect Match (PM), a simple methodology based on minibatch matching for learning neural representations for counterfactual inference in settings with any number of treatments. On the News-4/8/16 datasets with more than two treatments, PM consistently outperformed all other methods - in some cases by a large margin - on both metrics with the exception of the News-4 dataset, where PM came second to PD. Causal effect inference with deep latent-variable models. We presented PM, a new and simple method for training neural networks for estimating ITEs from observational data that extends to any number of available treatments. In this paper, we propose Counterfactual Explainable Recommendation ( Fair machine learning aims to mitigate the biases of model predictions against certain subpopulations regarding sensitive attributes such as race and gender. As a secondary metric, we consider the error ATE in estimating the average treatment effect (ATE) Hill (2011). To determine the impact of matching fewer than 100% of all samples in a batch, we evaluated PM on News-8 trained with varying percentages of matched samples on the range 0 to 100% in steps of 10% (Figure 4). PD, in essence, discounts samples that are far from equal propensity for each treatment during training. PM is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. data is confounder identification and balancing. Authors: Fredrik D. Johansson. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In contrast to existing methods, PM is a simple method that can be used to train expressive non-linear neural network models for ITE estimation from observational data in settings with any number of treatments. (2007), BART Chipman etal. Edit social preview. You can also reproduce the figures in our manuscript by running the R-scripts in. 372 0 obj compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. Sign up to our mailing list for occasional updates. A Simple Method for Learning Representations For Counterfactual You can download the raw data under these links: Note that you need around 10GB of free disk space to store the databases. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. We found that running the experiments on GPUs can produce ever so slightly different results for the same experiments. We assigned a random Gaussian outcome distribution with mean jN(0.45,0.15) and standard deviation jN(0.1,0.05) to each centroid. stream PMLR, 1130--1138. Since we performed one of the most comprehensive evaluations to date with four different datasets with varying characteristics, this repository may serve as a benchmark suite for developing your own methods for estimating causal effects using machine learning methods. Come up with a framework to train models for factual and counterfactual inference. (2007). Bottou, Lon, Peters, Jonas, Quinonero-Candela, Joaquin, Charles, Denis X, Chickering, D Max, Portugaly, Elon, Ray, Dipankar, Simard, Patrice, and Snelson, Ed. In particular, the source code is designed to be easily extensible with (1) new methods and (2) new benchmark datasets. We used four different variants of this dataset with k=2, 4, 8, and 16 viewing devices, and =10, 10, 10, and 7, respectively. Hw(a? Batch learning from logged bandit feedback through counterfactual risk minimization. 373 0 obj Learning representations for counterfactual inference | Proceedings of experimental data. We report the mean value. By using a head network for each treatment, we ensure tj maintains an appropriate degree of influence on the network output. Free Access. How does the relative number of matched samples within a minibatch affect performance? Scikit-learn: Machine Learning in Python. This repo contains the neural network based counterfactual regression implementation for Ad attribution. The ATE measures the average difference in effect across the whole population (Appendix B). dimensionality. (2007) operate in the potentially high-dimensional covariate space, and therefore may suffer from the curse of dimensionality Indyk and Motwani (1998). On IHDP, the PM variants reached the best performance in terms of PEHE, and the second best ATE after CFRNET. This repository contains the source code used to evaluate PM and most of the existing state-of-the-art methods at the time of publication of our manuscript. For each sample, the potential outcomes are represented as a vector Y with k entries yj where each entry corresponds to the outcome when applying one treatment tj out of the set of k available treatments T={t0,,tk1} with j[0..k1]. You signed in with another tab or window. Propensity Score Matching (PSM) Rosenbaum and Rubin (1983) addresses this issue by matching on the scalar probability p(t|X) of t given the covariates X. In thispaper we propose a method to learn representations suitedfor counterfactual inference, and show its efcacy in bothsimulated and real world tasks. Hill, Jennifer L. Bayesian nonparametric modeling for causal inference. Domain adaptation: Learning bounds and algorithms. This regularises the treatment assignment bias but also introduces data sparsity as not all available samples are leveraged equally for training. https://dl.acm.org/doi/abs/10.5555/3045390.3045708. Matching methods estimate the counterfactual outcome of a sample X with respect to treatment t using the factual outcomes of its nearest neighbours that received t, with respect to a metric space. Shalit etal. Zemel, Rich, Wu, Yu, Swersky, Kevin, Pitassi, Toni, and Dwork, Cynthia. We repeated experiments on IHDP and News 1000 and 50 times, respectively. However, it has been shown that hidden confounders may not necessarily decrease the performance of ITE estimators in practice if we observe suitable proxy variables Montgomery etal. The root problem is that we do not have direct access to the true error in estimating counterfactual outcomes, only the error in estimating the observed factual outcomes. Yiquan Wu, Yifei Liu, Weiming Lu, Yating Zhang, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang*. << /Filter /FlateDecode /S 920 /O 1010 /Length 730 >> (2018) and multiple treatment settings for model selection. Add a GitHub - ankits0207/Learning-representations-for-counterfactual To address the treatment assignment bias inherent in observational data, we propose to perform SGD in a space that approximates that of a randomised experiment using the concept of balancing scores. A kernel two-sample test. However, in many settings of interest, randomised experiments are too expensive or time-consuming to execute, or not possible for ethical reasons Carpenter (2014); Bothwell etal. Add a Want to hear about new tools we're making? Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks d909b/perfect_match ICLR 2019 However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatments, or both. functions. Learning representations for counterfactual inference - ICML, 2016. Copyright 2023 ACM, Inc. Learning representations for counterfactual inference. ICML'16: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48. Following Imbens (2000); Lechner (2001), we assume unconfoundedness, which consists of three key parts: (1) Conditional Independence Assumption: The assignment to treatment t is independent of the outcome yt given the pre-treatment covariates X, (2) Common Support Assumption: For all values of X, it must be possible to observe all treatments with a probability greater than 0, and (3) Stable Unit Treatment Value Assumption: The observed outcome of any one unit must be unaffected by the assignments of treatments to other units. Perfect Match is a simple method for learning representations for counterfactual inference with neural networks. Dorie, Vincent. cq?g We evaluated the counterfactual inference performance of the listed models in settings with two or more available treatments (Table 1, ATEs in Appendix Table S3). This is sometimes referred to as bandit feedback (Beygelzimer et al.,2010). 1) and ATE (Appendix B) for the binary IHDP and News-2 datasets, and the ^mPEHE (Eq. (2017) that use different metrics such as the Wasserstein distance. 3) for News-4/8/16 datasets. Assessing the Gold Standard Lessons from the History of RCTs. Estimation and inference of heterogeneous treatment effects using Examples of representation-balancing methods are Balancing Neural Networks Johansson etal. realized confounder balancing by treating all observed variables as PM effectively controls for biased assignment of treatments in observational data by augmenting every sample within a minibatch with its closest matches by propensity score from the other treatments. The IHDP dataset is biased because the treatment groups had a biased subset of the treated population removed Shalit etal. Although deep learning models have been successfully applied to a variet MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population, Perfect Match: A Simple Method for Learning Representations For questions, such as "What would be the outcome if we gave this patient treatment $t_1$?". Your file of search results citations is now ready. Another category of methods for estimating individual treatment effects are adjusted regression models that apply regression models with both treatment and covariates as inputs. Upon convergence at the training data, neural networks trained using virtually randomised minibatches in the limit N remove any treatment assignment bias present in the data. the treatment effect performs better than the state-of-the-art methods on both ITE estimation from observational data is difficult for two reasons: Firstly, we never observe all potential outcomes. {6&m=>9wB$ (2018) address ITE estimation using counterfactual and ITE generators. (2017), and PD Alaa etal. The experiments show that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes from observational data. Causal Multi-task Gaussian Processes (CMGP) Alaa and vander Schaar (2017) apply a multi-task Gaussian Process to ITE estimation. MarkR Montgomery, Michele Gragnolati, KathleenA Burke, and Edmundo Paredes. Limits of estimating heterogeneous treatment effects: Guidelines for ^mPEHE Brookhart, and Marie Davidian. Repeat for all evaluated method / degree of hidden confounding combinations. Christos Louizos, Uri Shalit, JorisM Mooij, David Sontag, Richard Zemel, and =1(k2)k1i=0i1j=0^PEHE,i,j Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. Chipman, Hugh A, George, Edward I, and McCulloch, Robert E. Bart: Bayesian additive regression trees. Propensity Dropout (PD) Alaa etal. The advantage of matching on the minibatch level, rather than the dataset level Ho etal. Mansour, Yishay, Mohri, Mehryar, and Rostamizadeh, Afshin. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Empirical results on synthetic and real-world datasets demonstrate that the proposed method can precisely decompose confounders and achieve a more precise estimation of treatment effect than baselines. The central role of the propensity score in observational studies for Treatment effect estimation with disentangled latent factors, Adversarial De-confounding in Individualised Treatment Effects Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. xcbdg`b`8 $S&`6Ah :H) @DH301?e`%x]0 > ; (2016). See below for a step-by-step guide for each reported result. synthetic and real-world datasets. For IHDP we used exactly the same splits as previously used by Shalit etal. RVGz"y`'o"G0%G` jV0g$s"w)+9AP'$w}0WN 9A7qs8\*QP&l6P$@D@@@\@ u@=l{9Cp~Q8&~0k(vnP?;@ PM is easy to implement, Learning Representations for Counterfactual Inference choice without knowing what would be the feedback for other possible choices. PM and the presented experiments are described in detail in our paper. Your search export query has expired. We trained a Support Vector Machine (SVM) with probability estimation Pedregosa etal. Learning Decomposed Representation for Counterfactual Inference His general research interests include data-driven methods for natural language processing, representation learning, information theory, and statistical analysis of experimental data. Rubin, Donald B. Estimating causal effects of treatments in randomized and nonrandomized studies. A supervised model navely trained to minimise the factual error would overfit to the properties of the treated group, and thus not generalise well to the entire population. In the first part of this talk, I will present my completed and ongoing work on how computers can learn useful representations of linguistic units, especially in the case in which units at different levels, such as a word and the underlying event it describes, must work together within a speech recognizer, translator, or search engine. We found that including more matches indeed consistently reduces the counterfactual error up to 100% of samples matched. Are you sure you want to create this branch? an exact match in the balancing score, for observed factual outcomes. [HJ)mD:K`G?/BPWw(a&ggl }[OvP ps@]TZP?x ;_[YN^0'5 Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. Domain adaptation: Learning bounds and algorithms. Doubly robust policy evaluation and learning. =0 indicates no assignment bias. Representation Learning: What Is It and How Do You Teach It? (2016) that attempt to find such representations by minimising the discrepancy distance Mansour etal. practical algorithm design. In, Strehl, Alex, Langford, John, Li, Lihong, and Kakade, Sham M. Learning from logged implicit exploration data. 36 0 obj << $ @?g7F1Q./bA!/g[Ee TEOvuJDF QDzF5O2TP?5+7WW]zBVR!vBZ/j#F y2"o|4ll{b33p>i6MwE/q {B#uXzZM;bXb(:#aJCeocD?gb]B<7%{jb0r ;oZ1KZ(OZ2[)k0"1S]^L4Yh-gp g|XK`$QCj 30G{$mt The coloured lines correspond to the mean value of the factual error (, Change in error (y-axes) in terms of precision in estimation of heterogenous effect (PEHE) and average treatment effect (ATE) when increasing the percentage of matches in each minibatch (x-axis). Estimation and inference of heterogeneous treatment effects using random forests. Estimating individual treatment effect: Generalization bounds and Rg b%-u7}kL|Too>s^]nO* Gm%w1cuI0R/R8WmO08?4O0zg:v]i`R$_-;vT.k=,g7P?Z }urgSkNtQUHJYu7)iK9]xyT5W#k ecology. Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. simultaneously 2) estimate the treatment effect in observational studies via stream In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. We perform experiments that demonstrate that PM is robust to a high level of treatment assignment bias and outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes across several benchmark datasets. Wager, Stefan and Athey, Susan. Several new mode, eg, still mode, reference mode, resize mode are online for better and custom applications.. Happy to see more community demos at bilibili, Youtube and twitter #sadtalker.. Changelog (Previous changelog can be founded here) [2023.04.15]: Adding automatic1111 colab by @camenduru, thanks for this awesome colab: . Jonas Peters, Dominik Janzing, and Bernhard Schlkopf. Once you have completed the experiments, you can calculate the summary statistics (mean +- standard deviation) over all the repeated runs using the. Matching as nonparametric preprocessing for reducing model dependence We consider fully differentiable neural network models ^f optimised via minibatch stochastic gradient descent (SGD) to predict potential outcomes ^Y for a given sample x. BART: Bayesian additive regression trees. The variational fair auto encoder. PSMPM, which used the same matching strategy as PM but on the dataset level, showed a much higher variance than PM. We performed experiments on several real-world and semi-synthetic datasets that showed that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes. arXiv Vanity renders academic papers from We therefore conclude that matching on the propensity score or a low-dimensional representation of X and using the TARNET architecture are sensible default configurations, particularly when X is high-dimensional. (2017) is another method using balancing scores that has been proposed to dynamically adjust the dropout regularisation strength for each observed sample depending on its treatment propensity. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research. We can not guarantee and have not tested compability with Python 3. (2017).. learning. After the experiments have concluded, use. Natural language is the extreme case of complex-structured data: one thousand mathematical dimensions still cannot capture all of the kinds of information encoded by a word in its context. In stream Speaker: Clayton Greenberg, Ph.D. All other results are taken from the respective original authors' manuscripts. Login. Bigger and faster computation creates such an opportunity to answer what previously seemed to be unanswerable research questions, but also can be rendered meaningless if the structure of the data is not sufficiently understood. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. A tag already exists with the provided branch name. Counterfactual inference enables one to answer "What if. endobj 4. We evaluated PM, ablations, baselines, and all relevant state-of-the-art methods: kNN Ho etal. In addition, we assume smoothness, i.e. Chernozhukov, Victor, Fernndez-Val, Ivn, and Melly, Blaise. Similarly, in economics, a potential application would, for example, be to determine how effective certain job programs would be based on results of past job training programs LaLonde (1986). in Language Science and Technology from Saarland University and his A.B. https://archive.ics.uci.edu/ml/datasets/Bag+of+Words, 2008. Perfect Match: A Simple Method for Learning Representations For This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In. Use of the logistic model in retrospective studies. As training data, we receive samples X and their observed factual outcomes yj when applying one treatment tj, the other outcomes can not be observed. $ ?>jYJW*9Y!WLPD vu{B" j!P?D ; =?5DEE@?8 7@io$. We found that PM handles high amounts of assignment bias better than existing state-of-the-art methods. %PDF-1.5 Small software tool to analyse search results on twitter to highlight counterfactual statements on certain topics, This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. Rubin, Donald B. Causal inference using potential outcomes. We did so by using k head networks, one for each treatment over a set of shared base layers, each with L layers. endobj This indicates that PM is effective with any low-dimensional balancing score. [Takeuchi et al., 2021] Takeuchi, Koh, et al. (2016), TARNET Shalit etal. The script will print all the command line configurations (450 in total) you need to run to obtain the experimental results to reproduce the News results. ^mATE To elucidate to what degree this is the case when using the matching-based methods we compared, we evaluated the respective training dynamics of PM, PSMPM and PSMMI (Figure 3). Correlation analysis of the real PEHE (y-axis) with the mean squared error (MSE; left) and the nearest neighbour approximation of the precision in estimation of heterogenous effect (NN-PEHE; right) across over 20000 model evaluations on the validation set of IHDP. The script will print all the command line configurations (2400 in total) you need to run to obtain the experimental results to reproduce the News results. https://archive.ics.uci.edu/ml/datasets/bag+of+words. data that has not been collected in a randomised experiment, on the other hand, is often readily available in large quantities. 2011. In. (2017); Schuler etal. To perform counterfactual inference, we require knowledge of the underlying. To compute the PEHE, we measure the mean squared error between the true difference in effect y1(n)y0(n), drawn from the noiseless underlying outcome distributions 1 and 0, and the predicted difference in effect ^y1(n)^y0(n) indexed by n over N samples: When the underlying noiseless distributions j are not known, the true difference in effect y1(n)y0(n) can be estimated using the noisy ground truth outcomes yi (Appendix A). The ^NN-PEHE estimates the treatment effect of a given sample by substituting the true counterfactual outcome with the outcome yj from a respective nearest neighbour NN matched on X using the Euclidean distance. xc```b`g`f`` `6+r @0AcSCw-_0 @ LXa>dx6aTglNa i%d5X{985,`Q`~ S 97L?d25h~a ;-dtc 8:NDZ9sUw{wo=s3W9=54r}I$bcg8y7Z{)4#$'ee u?T'PO+!_,zI2Y-Lm47}7"(Dq#^EYWvDV5o^r-*Yt5Pm@Wt>Ks^8$pUD.r#1[Ir << /Names 366 0 R /OpenAction 483 0 R /Outlines 470 0 R /PageLabels << /Nums [ 0 <> 1 <> 4 <> 5 <> 6 <> 7 <> 11 <> 14 <> 16 <> 20 <> 25 <> 30 <> 32 <> 34 <> 35 <> 39 <> 40 <> 44 <> 49 <> 50 <> 54 <> 57 <> 61 <> 64 <> 65 <> 69 <> 70 <> 77 <> ] >> /PageMode /UseOutlines /Pages 469 0 R /Type /Catalog >>